The arms race

The world is in the grip of an unprecedented AI arms race, with the US and China surging ahead in a contest that could reshape our societies from the ground up, new data reveals.

Why this story?

Artificial intelligence already touches our lives every day. But it also defines how economies will grow, how wars are fought and how the big issues are solved – from healthcare to the climate emergency and much, much more.

A year ago we decided that Tortoise should measure how nations are getting ready for a future driven by artificial intelligence. We brought together an expert panel to help us construct a ranking that reveals how AI is really evolving, the people who are driving it, and what it will take for countries to advance.

We hope the Index will also bring deeper understanding of how AI is transforming the world – and help ensure the technology supports us rather than divides us. James Harding, editor.

More than 10,000 artificial intelligence (AI) companies have been founded since 2015, attracting private funding of $37 billion, and thousands of extra programmers have been drafted onto AI projects globally in the last three years as demand for the technology soars, a new index by Tortoise Intelligence shows.

AI technology simulates human intelligence to process information faster than conventional computers – often by learning from its mistakes. It has the potential to transform multiple industries from healthcare to finance, but has also been used to covertly monitor populations, develop deadly weaponry and transform the labour market.

Elon Musk, the billionaire entrepreneur, and Sir Stephen Hawking, the theoretical physicist, have both warned about the dangers of such “thinking machines”. In 2018 Musk said that AI was more dangerous than nuclear weapons and called for global regulation: “It’s capable of vastly more than almost anyone knows and the rate of improvement is exponential.” Hawking, who died last year, warned in 2014 that AI could “spell the end of the human race”.

To investigate this shift, the Global AI Index by Tortoise Intelligence has ranked 54 countries based on their AI capabilities, measuring performance across 150 indicators including research, coding platforms, investment and government spending. For the first time, it discloses the huge acceleration of AI across the globe as the technology becomes a new battleground for influence and power.

Since the Canadian government issued the first national AI strategy in 2017, at least 30 more countries have followed suit, our data shows. The number of AI companies has doubled in four years, with almost 20,000 now developing technologies ranging from self-driving cars to medical algorithms capable of detecting disease. Total investment in AI firms last year topped $26 billion – up from $7 billion in 2015 – according to Crunchbase, a business information platform.

On Github, the world’s biggest open source development platform, the number of Chinese contributions to AI code rose from 150 per year in 2015 to 13,000 per year today. Those from Americans rose from 7,000 to 42,000.

Tortoise Intelligence has developed the Index to further understanding among policy makers, entrepreneurs and the public of a new technology that some suggest is a breakthrough as remarkable as the discovery of electricity. “AI is one of the most important things humanity is working on,” Google CEO Sundar Pichai said last year. “It holds the potential for some of the biggest advances we are going to see.”

The language of AI

Artificial intelligence is the defining technology of our future. Here is a short linguistic guide to help you navigate the revolution.

Algorithm: a set of rules that a computer can follow, as a mindless drone, in order to find a solution.

used in…

Artificial intelligence: the science of programming intelligent machines with many algorithms so that they are able to do things that people would usually do – driving a car, or reading an MRI scan.

which spawned…

Machine learning: the part of artificial intelligence that’s all about moving from programming machines, to training machines to perform tasks.

and then…

Deep learning: A subset of machine learning, made up of algorithms that allow software to train itself to perform tasks such as image recognition or understanding human language.

and most recently…

Neural networks: an artificial intelligence system that mimics the way the human brain works – they cluster and classify data. Neural network is a metaphor for the structure of the machine learning algorithm with the nodes and links looking a lot like a simple diagram of the brain.

many of which are developed by coders who’ve taken…

Massive Online Open Courses (MOOC): an online learning course, offering the opportunity to pick up new skills – many of which are related to computing and artificial intelligence.

and collaborate on…

GitHub: one of the code sharing and publishing services used by developers working on software for artificial intelligence, where coders around the world collaborate on projects.

where you might find code for…

Fundamental platforms: a toolkit for building intelligent applications; some offering pre-built algorithms. Word is to a word doc, as a fundamental platform is to artificial intelligence software.

which may well be written in…

Python: a general purpose programming language used by lots of artificial intelligence developers; the use of which is a great indicator of how many developers are out there and what they are doing.

and require a supportive…

Operating environment: the context for artificial intelligence in terms of rules, restrictions, trust and opinion that make technologies harder or easier to adopt.

part of which involves…

Kaggle: a competition to make the best data science and artificial intelligence models in town, solving specific problems like predicting the winners of a horse race, or recognising blurry images.

StackOverflow: a question and answer site for professional and enthusiast programmers.

Over 12 months, we measured 54 countries across seven key indicators: talent; infrastructure; operating environment; research; development; government strategy; and commercial ventures. Each indicator was weighted for importance after consultation with experts across the field.

The AI Index: key findings

The US is the undisputed leader in AI development, the Index shows. The western superpower scored almost twice as highly as second-placed China, thanks to the quality of its research, talent and private funding. America was ahead on the majority of key metrics – and by a significant margin. However, on current growth experts predict China will overtake the US in just five to 10 years.

China is the fastest growing AI country, our Index finds, overtaking the UK on metrics ranging from code contributions to research papers in the past two years. Last year, 85 per cent of all facial recognition patents were filed in China, as the communist country tightened its grip on the controversial technology. Beijing has already been condemned for using facial recognition to track and profile ethnic Muslims in its western region.

Britain is in third place thanks to a vibrant AI talent pool and an excellent academic reputation. This country has spawned hugely successful AI companies such as DeepMind, a startup founded in 2010 which was bought by Google four years later for $500 million. Britain has been held back, however, by one of the slowest patent application process in any of the 51 countries. Other countries are snapping at its heels.

Other findings include:

Despite playing a starring role in the space race and the nuclear arms race, Russia is a small player in the AI revolution, our data suggests. The country only comes 30th out of 54 nations, pushed down by its failure to attract top talent, and by a lack of research. Anxious to catch up, President Vladimir Putin announced last year a new centre for artificial intelligence hosted at the Moscow Institute for Physics and Technologies.

Smaller countries – such as Israel, Ireland, New Zealand and Finland – have developed vibrant AI economies thanks to flexible visa requirements and positive government intervention. Israel’s Mobileye Vision Technology, which provides technology for autonomous vehicles, was purchased in 2017 by Intel for $15.3 billion.

More than $35 billion has been publicly earmarked by governments to spend on AI development over the next decade, with $22 billion promised by China alone. Many more billions may have been allocated secretly through defence departments which are not made public.

Countries are using AI in very different ways. Russia and Israel are among the countries focusing AI development on military applications. Japan, by contrast, is predominantly using the technology to cope with its ageing population.

Multiple nations have expanded their AI capabilities as ministers realise that attracting top AI talent and research is dependent on government-led investment.

This month, the government of Jair Bolsonaro in Brazil announced the creation of eight new AI labs, with one working in direct partnership with the Brazilian army. “Since we came to the government, this has been among the priority plans to improve the country’s capacity for AI,” the country’s science minister said.

Nigeria is pushing out AI initiatives too, announcing a new agency for Robotics and Artificial Intelligence, while Slovenia have announced an International AI Research Centre in partnership with Unesco. Last month, Hungary’s Minister for Innovation and Technology announced the establishment of the Centre of Excellence in Artificial Intelligence.

The seven pillars

We measured the 54 countries against these key indicators.

Research

In 2010, US authors in top-rated AI journals outnumbered Chinese counterparts by two to one. That ratio has now reversed. Last year, 1,073 AI experts based at Chinese universities were credited in AI journals such as the Institute of Electrical and Electronics Engineers’s Transactions on Neural Networks, compared to 492 US authors. Australia and Israel also do well on this metric.

When experts are ranked according to their ‘H-index’ – a metric of productivity and the citation impact of the publications of a scientist or scholar – Americans occupy 626 of the 1,000 top spots, including all of the top ten spots at the time of our analysis. New Zealand, Saudi Arabia and Finland’s AI academics are also highly ranked.

Talent

AI-related activity on online coding platforms has skyrocketed globally over the last decade. Code related to AI on GitHub is now being edited 50 times more frequently than five years ago, with developers from China and India claiming a bigger share each year.

But the picture changes radically when adjusting for population. Small nations like Singapore, Israel and Estonia are revealed to be coding powerhouses when examining on a per capita basis, whether it be downloads of Python, one of the biggest languages for data science, or R, a widely used statistical software package.

Citizens of developing countries in particular are seeking AI education elsewhere: from MOOCs, ‘massive open online courses’. The top visitors to sites like Coursera and edX, which offer a wealth of data and AI-related lessons remotely, are from India, Turkey and Brazil, according to our analysis of web traffic via Alexa Rank.

Commercial ventures

The US is king of the commercial AI world: a key determinant of AI capability over all. US AI firms have raised $65 billion since 2000 but almost a quarter of that funding – $15 billion – came last year. Our commercial data comes from Crunchbase, a platform for finding business information, so these figures are biased towards Western companies, however.

In 2010, AI companies generated $676 million in funding rounds with investors. Last year, the figure was $26 billion.

Development

Last year, seven out of every 10 AI patents were filed in China, compared to just one in two in 2015. Patent applications are going up around the world, with 18 times more AI patents now filed globally each year compared to a decade ago.

Patent offices are also getting faster at approving AI patents. In 2015 AI-related patents granted had been waiting for approval on average for 800 days. For those granted in 2018, the wait dropped to 300 days.

Collaboration between AI experts in China and the US is at an all time high, both in terms of patents and jointly-written journal pieces. China’s tech giants, Alibaba, Baidu, Tencent and Huawei, are increasingly working with US academics. Some 6,595 patents filed by China’s tech giants have had at least one American inventor.

Infrastructure

Over half of the world’s 500 fastest supercomputers reside in either China, Japan, South Korea, Singapore, Taiwan and Hong Kong.

East Asian and Pacific countries also have an average download speeds of 33 mbps, including the two highest fliers in our index: Taiwan with 85 mbps and Singapore 71 mbps. While North American and European countries are not far behind in terms of download speed, countries in Latin America, the Middle East and Sub-Saharan Africa are all constrained by speeds of as low as 5.86 mbps.

As tech giants from Amazon to Alibaba offer more capabilities from the cloud, domestic computing power is becoming less relevant, however, especially to the world’s smaller AI nations.

Operating environment

Canada has one of the fastest visa processing times in the Index: just two weeks and the offer of a right to bring dependents. This policy has enabled the North American nation has become a destination for mobile AI professionals. By contrast, a data scientist hoping to work abroad in the Index’s two worst performers – Egypt and Pakistan – faces a wait of six months for a visa and has no right to bring family.

China overwhelmingly leads on public confidence in AI, with 70 per cent of respondents to a 2018 IPSOS survey saying they trust the technology. This contrasts with the situation in the West, where only 23 per cent of UK citizens and 25 per cent of Americans trust AI. China correspondingly has higher adoption rates for AI, although trust in AI seems to have less of a correlative effect on how well a country develops the technology in the first place.

Government strategy

China has earmarked over one and a half times more public money for AI than every other country in the world combined. However, countries’ public commitments are spread over different horizons, and China’s is the longest. In its national AI strategy published in 2017, Beijing sets aside $22.4 billion of funding over the next 13 years. The US, in contrast, earmarked $2 billion over 5 years; the same as Russia and slightly more than the UK. In India, only $421 million has been set aside. Much AI research will go into sensitive defence projects which will not publicly be disclosed.

“We are facing a widespread deployment of artificial intelligence in business and government,” Prince Zeid Ra’ad Al Hussein, former UN human rights chief, told us. “It’s important that we make this transition in a democratic, transparent and fair way. That’s what makes information underpinning the Global AI Index so vital – it gives us a basis for comparison, and highlights the areas where this is really working, and where more can be done.”

In India, thousands of citizens have enrolled in AI-based MOOCs – or “massive open online courses” – signalling a democratisation of education around computing. In terms of talent, India comes third overall. But the country ranks only 13th on investment and on other factors such as infrastructure, operating environment and research, it lies in the bottom half.

Choices made by national governments around AI policy will shape societies for years to come, experts predict. The global AI arms race is just heating up.

Further reading

An index is a ranking built from a careful selection of different measurements around a central topic or theme. Here, the index ranks countries on the basis of their capacity for artificial intelligence.

Which countries are we covering?

We limited our analysis to 54 countries, most of which had published some sort of national strategy on AI setting out future plans.

What’s included?

We grouped these data points into three clusters: innovation, implementation and investment. Within those are seven key categories: research, development, talent, operating environment, infrastructure, commercial ventures and government strategy – all of which contribute to overall AI capacity. We weighted the factors by their significance, and gave a preference to human and intellectual capital, as well as investment, as these as critical drivers to building capacity.